Behavior planning for autonomous vehicles in yield scenarios

ABSTRACT

In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

BACKGROUND

Advances in machine-vision methods, neural network architectures, andcomputational substrates are beginning to enable autonomous vehicles,such as but not limited to land-based autonomous vehicles (e.g.,self-driving cars and trucks). For the public and governmentalregulatory agencies to accept a wide deployment of self-driving cars andtrucks on roadways, the self-driving cars and trucks must achieve asafety-level that surpasses the current safety-level of an average humandriver. Safe and effective driving requires drivers to have confidencethat other vehicles in the area will appropriately yield when obligated.If a vehicle fails to yield, drivers of other nearby vehicles may beunable to proceed in a safe and efficient manner due to the“unpredictability” of other drivers, e.g., drivers that have provided abehavioral cue that they may fail to yield when obligated. Thus, anecessary condition for the deployment of self-driving cars and trucksincludes the capability to successfully, safely, and “politely”negotiate yield scenarios—scenarios in which yielding may be appropriate(e.g., intersections and merging lanes).

Typically, the local traffic regulations and driving norms and practicesof the area dictate which vehicle operators (and under what conditions)have a responsibility or obligation to yield to others. Such regulationsinclude traffic laws (e.g., vehicles must yield to pedestrians at acrosswalk), signage specific to the situation (e.g., a street sign thatindicates which in-roads to an intersection have a responsibility toyield to other in-roads), and other real-time cues (e.g., anear-simultaneous arrival of multiple cars at a traffic circle).However, conventional autonomous vehicles are incapable of encoding anddeploying such protocols. Instead conventional systems may aim to avoidcollisions while failing to account for yielding protocols, andtherefore cannot safely and predictably navigate yield scenarios, orconversely, yield indefinitely until no other vehicles are in the regionof contention.

SUMMARY

Embodiments of the present disclosure relate to behavior planning forautonomous vehicles in yield scenarios. Systems and methods aredisclosed that provide the real-time control of land-based autonomousvehicles when the vehicles are approaching a yield scenario.

In contrast to conventional systems, such as those described above,disclosed embodiments enable autonomous vehicles to negotiate yieldscenarios in a safe and predictable manner. In at least one embodiment,a yield scenario may be identified for a first vehicle by analyzingsensor data generated by a sensor of the first vehicle in anenvironment. One or more wait elements may be received in associationwith the yield scenario. A wait element may be a wait element datastructure. The wait element data structure may encode various aspects ofthe associated yield scenario. A wait element may encode a wait state,wait geometry, and/or ego information for a particular contentionbetween the ego and a contender. A wait element may be used to encode afirst path for the first vehicle to traverse a yield area in theenvironment, and a second path for a second vehicle to traverse theyield area. The first path may be used to determine a first trajectoryin the yield area for the first vehicle based at least on a firstlocation of the first vehicle at a time (e.g., a current time). Thesecond path may be used to determine a second trajectory in the yieldarea for the second vehicle based at least on a second location of thesecond vehicle at the time. The trajectories may be used to evaluatewhether there is a conflict between the first trajectory and the secondtrajectory based on a wait state associated with the wait element. Byevaluating the potential for conflicts between the two trajectories, thewait state can be used to define a yielding behavior for the firstvehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for behavior planning for autonomousvehicles in yield scenarios are described in detail below with referenceto the attached drawing figures, wherein:

FIG. 1 is an example of a yield planner system, in accordance with someembodiments of the present disclosure;

FIG. 2 is an example of a state machine, corresponding to a stop atentry then yield composite wait state, in accordance with someembodiments of the present disclosure;

FIG. 3 is a flow diagram showing a method for controlling an autonomousvehicle (e.g., an ego vehicle), in accordance with some embodiments ofthe present disclosure;

FIG. 4 is a flow diagram showing a method for identifying crossings andmerges for claimed paths for a yield scenario, in accordance with someembodiments of the present disclosure;

FIG. 5 is a flow diagram showing a method for generating trajectoriesfor crossings and merges for a yield scenario, in accordance with someembodiments of the present disclosure;

FIG. 6 is a flow diagram showing a method for analyzing crossingtrajectories for a yield scenario, in accordance with some embodimentsof the present disclosure;

FIG. 7 is a flow diagram showing a method for analyzing mergingtrajectories for a yield scenario, in accordance with some embodimentsof the present disclosure;

FIG. 8A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 8B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 8A, in accordance with someembodiments of the present disclosure;

FIG. 8C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 8A, in accordance with someembodiments of the present disclosure;

FIG. 8D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 8A, in accordancewith some embodiments of the present disclosure;

FIG. 9 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure; and

FIG. 10 is a block diagram of an example data center suitable for use inimplementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to behavior planning forautonomous vehicles in yield scenarios. Although the present disclosuremay be described with respect to an example autonomous vehicle 800(alternatively referred to herein as “vehicle 800” or “ego-vehicle 800,”an example of which is described with respect to FIGS. 8A-8D), this isnot intended to be limiting. For example, the systems and methodsdescribed herein may be used by, without limitation, non-autonomousvehicles, semi-autonomous vehicles (e.g., in one or more adaptive driverassistance systems (ADAS)), piloted and un-piloted robots or roboticplatforms, warehouse vehicles, off-road vehicles, vehicles coupled toone or more trailers, flying vessels, boats, shuttles, emergencyresponse vehicles, motorcycles, electric or motorized bicycles,aircraft, construction vehicles, underwater craft, drones, and/or othervehicle types. In addition, although the present disclosure may bedescribed with respect to controlling a land-based autonomous vehiclefor negotiating a yield scenario, this is not intended to be limiting,and the systems and methods described herein may be used in augmentedreality, virtual reality, mixed reality, robotics, security andsurveillance, autonomous or semi-autonomous machine applications, and/orany other technology spaces where autonomous control systems may beused.

In the normal course of operating an autonomous vehicle, a control agentmust avoid both moving and non-moving obstacles (e.g., other vehicles,pedestrians, bicyclists, lane barriers, and the like). In addition toavoiding collisions, an agent has a fundamental responsibility to yieldto other roadway users in certain scenarios (e.g., “yieldingconditions”). Such yielding conditions may exist at (controlled andun-controlled) intersections, crosswalks, merging lanes, highway (orinterstate) on/off ramps, traffic circles, and the like such asnavigating parking structures and/or lots. In order to allow anotheruser to safely, confidently, and efficiently “clear” the yieldingcondition, yielding behavior may involve deaccelerating, or evenbringing the vehicle to a complete stop. For instance, in an unmarkedintersection where another car has previously arrived, a subsequentarriving car may deploy appropriate yielding behavior by slowing down toenable the first car to safely clear the intersection. Such yieldingbehavior insures that the subsequent car does not enter the intersectionuntil the first car has safely cleared the intersection. Under suchyield conditions, one or more users may have a clearly definedobligation (or responsibility) to yield to other users.

Yielding behavior provides utility beyond just avoiding collisions.Proper yielding behavior may insure “polite” and “expected” drivingdynamics, which are required for safe and efficient transportation. Forexample, even if an agent acts to avoid a potential collision under ayield condition (e.g., by accelerating through an intersection), failingto yield when there is an obligation to do so do creates tense andanxious driving conditions for all users in the area. Even if oneaccelerates to avoid a collision, an unyielding vehicle may generateanxiety, a feeling of danger, and anger (e.g., road rage) in otherdrivers, bicyclists, and pedestrians. That is, even if a collision isavoided by taking aggressive action; the collision was not avoided in a“safe and polite manner,” as expected by other users. Accordingly, inoperating an autonomous vehicle, an agent for the autonomous vehicle maybe obligated (e.g., either legally or normatively) to adopt one or morebehavioral yield strategies when approaching a yield scenario.

In contrast to conventional systems, the disclosure provides for a“yield planner” for an autonomous vehicle (“ego-vehicle”) that mayactively monitor for the arrival of one or more yield conditions (e.g.,the vehicle is arriving at an intersection, the vehicle is negotiatingan on/off ramp, or the vehicle is preparing to change lanes). When ayield condition is detected, the yield planner may determine appropriateyielding behavior (e.g., stop at entry, take way, etc.). When a controlagent for the ego-vehicle adopts the determined yield behavior, theego-vehicle may safely satisfy its required and expected yieldingobligations, while avoiding collisions.

In operation, the yield planner may analyze the relationship between theego-vehicle's longitudinal progress forward (e.g., forward-looking intime) and the longitudinal progress forward of other traffic actors(e.g., contenders). The yield planner may determine yielding behaviorfor the ego-vehicle and contenders, and predict whether contenders areyielding appropriately when expected. When approaching a drivingscenario that may involve yielding (e.g., a yield scenario), the yieldplanner may receive one or more wait elements that may encode suchinformation as one or more wait states (that define specific yieldbehavior), a set of “claimed paths” or lanes at least partially in ayield area for the ego-vehicle, as well as sets of claimed paths orlanes at least partially in the yield area for each contender relevantto the yield scenario.

Forward simulations of the claimed paths may be performed to generatetrajectories (paths may be assumed to exist outside of a temporaldimension, whereas trajectories may be embedded within a space-timemanifold). The trajectories may be tested to determine whether theego-vehicle's trajectory is within a yield area during the time that acontender's trajectory is within the yield area. The world may bemodeled as a two-dimensional spatial terrain, with spatial coordinates(x, y) and actors moving along in time t. For any time t, claimed setsof the vehicles may be considered. The claimed sets may also extend intime (in the future forward from t). Thus, the claimed sets may exist ina time-evolving 3D space-time manifold. Multiple different times may beconsidered, and thus the yield planner's analysis may operate in atleast a 4D space-time manifold having two spatial dimensions and twotemporal dimensions. The two temporal coordinates may be related, andthus may be collapsed into a single temporal coordinate, via thisrelationship. However, embodiments may keep the temporal axes separateto simplify the analysis of the vehicles' trajectories throughspace-time. The temporal coordinate related to sweeping out the claimedsets may be denoted as z, parameterizing the 3D space of claimed sets.

The yield planner's analysis may be divided into multiple stages. Afirst stage may find interference pairings (or contention points)between points on the paths in terms of crossings (represented as pathinterval pairs that are determined to interfere as a whole) and merges(represented as path interval pairs that are determined to be the sameor at least similar). A second stage may consider how actors and theirclaimed sets progress along their individual paths.

In the first stage, the claimed paths may be employed to identify allpotential interferences between the ego-vehicle and each of thecontenders. The physical extensions of the ego-vehicle and thecontenders may be modeled as bounding boxes (or shapes) around theclaimed paths. In the second stage, for each potential interference,multiple trajectories may be determined (each trajectory beginning atsubsequent temporal values). Each trajectory may be modeled as a secondorder dynamical equation that assumes upper bounds for the magnitude ofeach of the vehicles' accelerations. The dynamical equations may beemployed to determine an intersection in the temporal ranges thevehicles spend within the yield area (according to the modeledtrajectories). A yielding behavior may be selected for the ego-vehicle,depending upon whether the determined intersection of the temporalranges is the null set or is a non-empty set (e.g., to advance or remainon a wait state of a state machine of one or more wait states thatcontrol yielding behavior).

With reference to FIG. 1 , FIG. 1 is an example of a yield plannersystem 100, in accordance with some embodiments of the presentdisclosure. It should be understood that this and other arrangementsdescribed herein are set forth only as examples. Other arrangements andelements (e.g., machines, interfaces, functions, orders, groupings offunctions, etc.) may be used in addition to or instead of those shown,and some elements may be omitted altogether. Further, many of theelements described herein are functional entities that may beimplemented as discrete or distributed components or in conjunction withother components, and in any suitable combination and location. Variousfunctions described herein as being performed by entities may be carriedout by hardware, firmware, and/or software. For instance, variousfunctions may be carried out by a processor executing instructionsstored in memory.

As noted above, performing a yielding behavior may be a required whenoperating a vehicle (e.g., a car, truck, motorcycle, scooter, or thelike) subject to “rules of the road.” A scenario or situation that givesrises to a required yielding behavior may be referred to as a yield (oryielding) scenario. Examples of yield scenarios include scenarios wheremultiple actors (e.g., vehicles, pedestrians, bicyclists, and the like)are present, and may include roadway elements such as but not limited tomulti-way intersections, merging lanes, off/on ramps for interstates orhighways, traffic lights and/or signs, crosswalks, construction zones,parking lots/structures, traffic circles, and the like. To ensure safeand polite navigation of roadways, a yield scenario may includelegally-enforced yield obligations and/or socially-enforced yieldobligations (e.g., normative yield obligations). When approaching ayield scenario, a control agent for an autonomous vehicle (e.g., firstvehicle 140) may employ the yield planner system 100 to detect the yieldscenario and determine a yield behavior for the vehicle that willsatisfy the autonomous vehicle's legal and normative yield obligations.

In general, the yield planner system 100 may receive sensor data,encoding one or more aspects of an environment 126, and use the sensordata to detect a yielding scenario. In response to detecting a yieldingscenario, the yield planner system 100 may determine appropriatebehavior (e.g., a yield behavior) for an example autonomous vehicle 140(alternatively referred to herein as a “first vehicle 140” or an“autonomous vehicle 140”), an example of which is described in moredetail herein with respect to FIGS. 8A-8D, in the event that a yieldscenario is detected using the sensor data. The determined yieldbehavior may be provided to a control system (or control agent) of theautonomous vehicle 140. The control agent may operate the vehicle 140 inaccordance with the determined yield behavior, such that the vehicle 140successfully navigates the yield scenario. In embodiments, successfullynavigating a yield scenario may include the vehicle 140 successfullyavoiding collisions with other actors associated with the yieldscenario, while simultaneously satisfying the vehicle's 140 legal andnormative yielding responsibilities, e.g., the vehicle 140 “clears” theyield scenario without incidence and in a “safe and polite manner,” asexpected by others. The yield planner system 100 may be a sub-system ofthe vehicle's 140 control system (e.g., the vehicle's control agent).For clarity and brevity, other components of the vehicle's 140 controlsystem, outside of the yield planner system 100, are not shown in FIG. 1.

The yield scenario depicted in the environment 126 is anintersection-based yield scenario (e.g., an intersection) that includesthe autonomous vehicle 140 approaching a yield area 130 (e.g., thecenter of the intersection where the autonomous vehicle 140 and one ormore other actors may potentially collide). In addition to theautonomous vehicle 140, the yield scenario (depicted in FIG. 1 )includes additional vehicles 142 and 144 approaching, arriving at,and/or navigating through the yield area 130. For clarity purposes, theautonomous vehicle 140 may be referred to as a first vehicle 140 (or anego-vehicle 140), the additional vehicle 142 may be referred to as asecond vehicle 142, and the additional vehicle 144 may be referred to asa third vehicle 144. Because embodiments are directed towardscontrolling the yield behavior of the first vehicle 140, other actorsthat the first vehicle must avoid (e.g., the second vehicle 142 and thethird vehicle 144) may be referred to as contenders.

Contenders may but need not be vehicles, and may include other actorssuch as, but not limited to pedestrians, bicyclists, and the like.Contenders that are generally constrained via a roadway may be referredto as non-holonomic contenders, while contenders with greaterdegrees-of-freedom (e.g., contenders that are not necessarilyconstrained by the roadway) may be referred to as holonomic contenders.For example, cars, trucks, and motorcycles may be referred to asnon-holonomic contenders, while pedestrians and bicyclists may bereferred to as holonomic contenders. In general, because of lesserdegrees-of-freedom (DOF), the forward-looking paths of non-holonomiccontenders may be predicted somewhat more confidently than the behaviorof holonomic contenders. Although not shown in FIG. 1 , the environment126 may include additional and/or alternative contenders, such asadditional non-holonomic contenders as well as one or more holonomiccontenders (e.g., one or more pedestrians in one or more cross-walks ofthe intersection). The combination of the vehicle 140 and each of therelevant contenders associated with a yield scenario may be collectivelyreferred to as the actors associated with the yield scenario.

It should be understood that the embodiments are not limited to a yieldscenario that includes a four-way intersection (e.g., the yield area130) with three vehicles (the first vehicle 140, the second vehicle 142,and the third vehicle 144) occupying three of the four prongs of theintersection. Rather, as discussed throughout, a yield scenario mayadditionally include intersections with less numbers ofincoming/outgoing paths (e.g., three way intersection, T-intersections,and the like), as well as intersections with greater numbers ofincoming/outgoing paths (e.g., five-way intersections, six-wayintersections, traffic circles, and the like). Additional yieldscenarios may include merging traffic (e.g., vehicle lane changes,off/on ramps for interstates/highways, and the like). Yield scenarioscan be classified into multiple categories: crossings (e.g., such asthat depicted in FIG. 1 ) and merges (e.g., traffic merging on an on/offramp).

Each actor may be subject to a finite maximum positive acceleration anda finite minimum negative acceleration, e.g., any vehicle's acceleratingand braking capabilities are finite. Assuming an actor's velocity isnon-zero (e.g., relative to the Earth's surface), then at any point intime, each actor may not be able to avoid a bounded set of spatialpoints due to momentum. The set of unavoidable spatial points may bedependent upon the finite acceleration and breaking capabilities of theactor, as well as the actor's current position in phase space (e.g., theactor's current spatial position and velocity, relative to the Earth'ssurface) and the finite reaction time of the agent's control system. Theset of unavoidable spatial points may be interchangeably referred to aclaimed points, a claimed set, claimed paths, and/or set of claimedpoints.

A continuous set of claimed points may define a claimed path that theactor is committed to traversing in the near future. In one or moreembodiments, the control system of the first vehicle 140 may be enabledto determine, at any point in time, the claimed paths of the firstvehicle 140, as well as to estimate the claimed paths for eachcontender. The control system of the first vehicle 140 may also beenabled to take any reasonable action to avoid claiming any point orpath that includes points that are already claimed by a contender,unless the contender “releases” their claimed points via an action(e.g., accelerates, decelerates, turns, etc.).

To perform these functionalities, the yield planner system 100 mayinclude, for example, a yield scenario detector 102, a wait elementreceiver 104, a trajectory generator 106, a scenario analyzer 110, ayield behavior predictor 112, and a control planner 114.

The yield scenario detector 102 is generally responsible for detecting ayield scenario. That is, the yield scenario detector 102 detects asituation where a crossing or a merging interference pattern has asignificant likelihood of arising. In some embodiments, the yieldscenario detector 102 is enabled to detect an intersection or a mergingyield scenario, where there is a substantial likelihood that the firstvehicle 140 has a yielding responsibility. In some embodiments, theyield scenario detector 102 may receive one or more signals from othercomponents of the first vehicle 140 to employ in the detection of ayield scenario. In at least one embodiment, the yield scenario detector102 may receive a yield signal, generated by another component, wherethe yield signal encodes the detection of an approaching yield scenario.Whether the yield scenario is detected directly by the yield scenariodetector 102, or by other components of the first vehicle, the yieldscenario may be detected by analyzing sensor data generated by sensorsof the first vehicle 140 in the environment 126. For example, a yieldscenario may be detected based at least on localizing the vehicle 140 toa map (e.g., using computer vision and/or GPS data), where the map mayidentity a location of a yield scenario with respect to the vehicle 140.Additionally or alternatively, computer vision may be used to classifyone or more locations in one or more images of the environment ascorresponding to a yield scenario. In at least one embodiment, the yieldscenario detector 102 may detect a yield scenario based at least ondetermining if a set of base rules (traffic rules) apply to a scene andprovide corresponding wait elements. In at least one embodiment, theyield scenario detector 102 may detect a yield scenario based at leaston applying rules encoded in the map, resolved with signal states ifapplicable (e.g., traffic signs, lights, etc.) and provide correspondingwait elements.

The wait element receiver 104 is generally responsible for receiving await element data structure. A portion of the first vehicle's 140operating system that is upstream from the yield planner system 100 maygenerate the wait element data structure. The wait element datastructure may encode various aspects of a yield scenario. Thus, the waitelement may be associated with a detected yield scenario. A wait elementmay encode a wait state, wait geometry, and/or ego information for aparticular contention between the ego and a contender. A wait group mayrepresent a group of wait elements for a particular yield area (e.g., anintersection, merge area, etc.) and/or scenario. In at least oneembodiment, all wait conditions in a wait group may need to beconsidered together and cleared together. This may prevent suchsituations as the vehicle 140 being stuck waiting for pedestrians at theend of a left turn while still in the path of oncoming traffic byconsidering the oncoming traffic contention together with the pedestriancrossing contention in the same wait group.

Wait geometry may correspond to geometry that results when applyinginformation about wait conditions or yield scenarios to a lane graph.Wait geometry may apply to an ego path (such as an entry line), acontender path (such as a contender area), or to a background context(such as the inside ground of an intersection, or the presence of anintersection entry line). Wait geometry may include: entry lines andexit lines (for both ego paths and contender paths), entry and exitcontender areas (for both ego paths and contender paths), anintersection entry line and inside ground (as part of the generalcontext of a wait group), and/or contention points between an ego pathand a contender path (in embodiments where an explicit encoding of oneof the crossing or merge points between paths is used). A speed limitmay be applied between an entry line and exit line. Each of these itemsmay be encoded as invalid to accommodate encoding wait conditions wherethe items do not apply (for example an on-ramp traffic light only has anego path and an entry line, but no exit line, contender path, or insideground). Another example would be an encoding of a new speed limit by await group containing only an entry line and a speed limit in theoverall context, and everything else set to invalid. The exit line maythen to be interpreted as infinite or until further notice, andsimilarly for other properties.

An entry line for an ego path may encode a stopping point for several ofthe yielding behaviors. An entry line may also signal a beginning of ageneral contention area (which may also be referred to as a yield area),and may be book-ended by an exit line. An exit line may be used todetermine which segment of an ego path needs to be cleared to clear await group of wait conditions. Wait geometry may also include insideground area, which may represent the inside ground of an intersection orother yield area as a polygonal area. In some cases, an inside groundarea may cover a segment between an entry line and an exit line(sometimes the exit line is moved out such as beyond a pedestriancrossing even though the inside ground is not). Entry contender areasand inside ground may provide context for analyzing other actors. Thismay be performed by the scenario analyzer 110 assigning actors to pathsand areas (in a non-mutually exclusive way). The geometry of ego andcontender paths as well as the contention point may be used by the yieldplanner system 100 to implement yielding as required. The geometry mayalso be used to determine which rules apply.

A contention point may represent an explicit geometric point, but alsorepresent a particular contention that a wait element is referring toand is encoding the state of In this sense, the state of contention at acontention point may represent a payload of a contention stateresolution process. Contention state resolution may provide, for eachcontention point, a determination of a manner in which the vehicle 140should or not yield (yield behavior) with respect to the contentionpoint. In this sense a contention point may be used to, given a choiceof ego path, access a contender path and via that contender path, actualcontenders and yield behavior relative to them.

In at least one embodiment, a wait element may include some subset ofthe wait geometry of one ego path, the wait geometry of one contenderpath, a wait geometry context, and a state of contention. One or more ofthese items can be encoded as invalid if it is not applicable. The waitelements may constitute ‘atoms’ of how information about wait conditionsare encoded so that they can be passed to the yield planner system 100.

The trajectory generator 106 is generally responsible for generating thetrajectories for the autonomous vehicle 140, and each of the othercontenders relevant to the yield scenario encoded in the one or morewait elements received by the wait element receiver 104. Thetrajectories generated by the trajectory generator 106 include 1Dstructures embedded within a 4D flat space-time manifold, as discussedherein. The scenario analyzer 110 is generally responsible for analyzingthe first vehicle's 140 one or more trajectories and one or moretrajectories for each contender in the yield scenario to determine thelikelihood of one or more potential collisions within anintersection-based yield scenario, such as but not limited to the oneshown in FIG. 1 .

The scenario analyzer 110 may additionally or alternatively analyze thefirst vehicle's 140 trajectories and the trajectories for each contenderin the yield scenario to determine the likelihood of one or morepotential collisions within a merge-based yield scenario (e.g., mergingonto a freeway). Based on the intersection or merging analysis performedby the scenario analyzer 110, the yield behavior predictor 112determines a yield behavior (or yield action), in which to control thefirst vehicle 140.

When approaching a yield scenario, the yield planner system 100 (via theyield behavior predictor 112) may perform a yielding analysis (based onthe analysis of the scenario analyzer 110 and/or scenario analyzer 110)to determine a yield behavior (e.g., a yield action) for the firstvehicle 140. When the vehicle 140 acts in accordance to the determinedyield behavior, the probability that any resulting claimed set of points(of the first vehicle 140) will intersect with the claimed set of eachof the other contenders is significantly reduced, even if one or morecontenders are not meeting their yield obligations. That is, the yieldplanner system 100 may ensure that, at least for yield scenarios, theclaimed sets of the first vehicle 140 do not intersect the claimed setsassociated with the contenders, even if the contenders do not activelycontribute to avoiding the first vehicle 140.

The yield planner system 100 may also ensure that the first vehicle's140 claimed set maintains a sufficient margin from interference with thecontenders' claimed sets. The margin is sufficient, such that while thefirst vehicle 140 is operated in accordance to the determined yieldbehavior, the navigation of the first vehicle 140 indicates to thecontenders that the first vehicle 140 is conforming to their legal andnormative yielding obligations.

In at least one embodiment, the yield behavior predictor 112 maydetermine yield behavior based at least on performing contention stateresolution for the yield scenario. This may include determininginformation associated with one or more of the first vehicle 140, thesecond vehicle 142, and/or the third vehicle 144 used to apply waitelements to yield scenarios, such as a classification (or type) of eachactor (e.g., holonomic v. non-holonomic), its coordinates in a relevantphase space (e.g., position and velocity components), and/or otherinformation used by the scenario analyzer 110 and/or the trajectorygenerator 106 to evaluate contention states of wait elements.

Contention state resolution may provide a contention state (which mayalso be referred to as a wait state or yield behavior) for each of thewait elements that apply to a yield scenario (e.g., one or more waitgroups). A contention state of a wait element may represent aninstruction corresponding to the manner in which the vehicle 140 shouldyield or proceed with right-of-way with respect to the wait element, asa matter of rule, expectation, formal or informal convention or norm(e.g., it may indicate what should happen according to conventionwhether it is actually happening or not).

The control planner 114 of the vehicle 140 is responsible for actuallyimplementing yielding behavior from the yield behavior predictor 112,considering what should happen based on wait states, whether the vehicle140 is in a position to stop and follow that instruction, whether otheractors appear to be fulfilling their expected yielding duties, and takeappropriate action. In at least one embodiment, the control planner 114may determine trajectories and prune a possibility or search space ofthe trajectories based at least on wait conditions or states, such asbased on determining the trajectories are incapable of complying withthe wait states. For example, if a trajectory or path extends beyond await entry line when an active wait state includes “stop at entry,” itmay be pruned. Then the control planner 114 may select from theremaining options based on other criteria, such as comfort and obstacleavoidance. In at least one embodiment, trajectories may be generated byapplying speed profiles to paths. The pruning may include pruning one ormore speed profiles.

The control planner 114 may perform such operations as determining thateven though a contention or wait state is “take (right-of-) way,”another vehicle is not yielding (essentially detecting‘appropriate-to-honk’) and decide to yield although that course ofaction may not be the prescribed course according to the yield behaviorpredictor 112. In addition, collision avoidance systems may always berunning, so that regardless of the state of contention and even what thecontrol planner 114 decides to do, collision avoidance may proceduresmay be deployed (e.g., if claimed sets are intersecting). The controlplanner 114 may implement yielding behavior by the yield behaviorpredictor 112 that analyzes all contentions in a wait group until theycan be cleared jointly. In at least one embodiment, given a set of waitelements and resolved wait states, the control planner 114 may beconfigured to use the most restrictive wait state to define expectedyielding behavior. For example, if one wait state is “take right-of-way”and another is “stop at entry,” the control planner 114 may determinethe vehicle 140 is to remain at the entry line.

Some non-limiting examples of wait states include: stop at entry, stopat entry then yield from entry, stop at entry then yield contentionpoint, yield from entry, and yield from entry transient. Additional waitstates include: stopped first has precedence, stop at entry thennegotiate, negotiate, take right-of-way, take way transient, stop atentry, yield contention point, and yield contention point transient. Insome embodiments, six of the above wait states may be considered as“primitive states,” and the other wait states may be generated via oneor more combinations of the six primitive states. The six primitivestates may include: take right-of-way, stop at entry, yield from entry,yield contention point, transient, and negotiate.

Take right-of-way may represent a directive for the vehicle 140 toexpect other actors to yield and may not impose any formal constraintfrom the contention (other than for the control planner 114 to watchother actors related to the contention and make sure they yield asexpected).

Take way transient may signal that a wait state is about to change. Inmay indicate that take right-of-way still applies, but is likely tochange soon to a more restrictive state. For example, a ‘yellow’ stateof a traffic light may trigger take way transient.

Yield from entry may represent a directive that until such time that thecontention is expected to be cleared, the vehicle 140 is to remain atthe entry line. In this case, a pre-stop may not be mandated by rule,but the yield behavior predictor 112 should make sure that thecontention is clear before the vehicle 140 passes the entry line, whichoften results in a pre-stop. Where the wait conditions in a wait areconsidered jointly, yield from entry may often mean that the yieldbehavior predictor 112 should be sure that all contentions in the waitgroup are clear before the vehicle 140 passes the entry line. In otherwords, if one contention/wait element has yield from entry, each mayinherit that when analyzed by the yield behavior predictor 112, and ifone has stop at entry, all wait elements may inherit the pre-stop.

Yield from entry transient may represent a transient version of yieldfrom entry. It may indicate that yield from entry still applies, but islikely to change soon to a more restrictive state. Similarly, yieldcontention point transient may refer to a transient version of yieldcontention point.

Negotiate may represent that there is no known basis to determine rightof way, such as for example for a highway merge where there is no cuefrom traffic rules, map statistics, geometry or size of the road (e.g.,equally large highways merging and with similarly straight shapes).

The primitive stop at entry wait state may require the first vehicle 140to stop at the entry wait line 132 of the intersection 130 and awaitfurther instructions. In contrast, the primitive yield contention (oryield contention point) wait state may indicate that a pre-stop is notmandated by a rule of the road (e.g., an uncontrolled intersection)and/or the first vehicle 140 is not legally obligated to stop and waitat the entry wait line 132 in order to clear the intersection 130.Rather, yield contention wait state may indicate that the first vehicle140 should yield to contenders that may be in contention with the firstvehicle (e.g., the second vehicle 142 and the third vehicle 144) andthat the first vehicle 140 does not proceed in a manner that would“block” the intersection 130 while the other contenders are negotiatingthe intersection 130. For instance, under a yield contention wait state,the first vehicle 140 may proceed by pulling forward to initiate aleft-hand turn, but slowly enough and with enough margin so thatoncoming traffic (e.g., the third vehicle 144) understands that thefirst vehicle's 140 intention is to yield, and obviously not get in theway of the oncoming traffic. As an example of how non-primitive waitstates (e.g., composite wait states) may be generated via combinationsof primitive wait states, the stop at entry then yield contention waitstate may include the behavior that the first vehicle follows the yieldscontention wait state behavior, while performing a pre-stop at the entrywait line 132. Examples include stop at entry then yield from entry,stop at entry then yield from entry transient, stop at entry then yieldcontention point, stop at entry then yield condition point transient,and stop at entry then negotiate.

Stopped first has precedence may represent that right-of-way (e.g., fora multi-way stop) is determined as a first-in-first-out queue where ‘in’is defined as coming close to the intersection (likely in thecorresponding contender area at the entry line pointing into the insideground) as the first actor from that contender path, and stopping. Thiswait state may imply further processing of ‘who-stopped-first’ toactually resolve into a take right-of-way or yield from entry wait stateper each actor associated with a contender path.

At least a portion of the wait states may be modeled as state machines,or at least states within a state machine. For example, the compositewait states may be modeled as wait states composed of statescorresponding to the wait states included in the composite wait state.With reference to FIG. 2 , FIG. 2 is an example of a state machine 200,corresponding to a stop at entry then yield composite wait state. Thestate machine 200 is initiated at state 202, where the primitive waitstate stop at entry is processed. The state machine 200 continues in theinitial state 202, while the speed of the ego vehicle (e.g., the firstvehicle 140 of FIG. 1 ) is less than 0.5 meters per second (m/s) and thedistance to the wait line is not in range. When the transition conditionis met (e.g., the ego speed is <0.5 meters per second and the distanceto the wait line is in range) the state machine 200 transitions to state204, where the ego vehicle is fully stopped at the entry to anintersection. When the ego vehicle has waited at least three seconds,the state machine 200 may transition to state 206. At state 206, theprimitive wait state yield contention may be processed. Upon completionof the yield contention wait state, the state machine 200 may be exited.

Returning our attention to FIG. 1 , the trajectory analysis performed bythe trajectory generator 106 may generate the temporal evolution of theclaimed sets, resulting in time-dependent trajectories for the firstvehicle 140 and each of the contenders. As discussed below, thetime-evolved trajectories are analyzed (by the scenario analyzer 110and/or the scenario analyzer 110) for each actor to detect potentialcollisions (or “near” collisions) between the trajectories of the firstvehicle 140 and the trajectories of the other actors. Such (near orexplicit) collisions (or intersections) may indicate a potentialcollision between the first vehicle 140 and the other actor. Note here,the term intersection may refer to one or more spatial points that areincluded in at least two trajectories. The yield behavior predictor 112determines a yield behavior (or wait state) for the first vehicle 140that, if followed, will likely avoid potential collisions, via thetrajectory analysis. For non-holonomic contenders, the yielding analysismay be a structured yielding analysis (e.g., because the non-holonomiccontender may behave in a structured manner due to the reduction in thedegrees of freedom (DOF)). For holonomic contenders, the yieldinganalysis may be an unstructured yielding analysis (e.g., because theholonomic contender may be behave in an unstructured manner due to theincrease in degrees of freedom). When the possible future trajectoriesfor holonomic contenders are constrained, or otherwise limited to arelatively small set of possible trajectories, the yielding analysis mayrevert to a structured yielding analysis.

Whether the analysis is structured or non-structured, the firstvehicle's 140 possible trajectories (as well as the contenders' possibletrajectories) are projected forward in time, and interference (e.g.,intersections) between trajectories of separate actors are detected. Twonon-limiting example types of interference patterns include crossingsand merges. The crossings or merges may be analyzed by the scenarioanalyzer 110. A crossing interference pattern may arise if both thefirst vehicle 140 and the second vehicle 142 attempt to, simultaneously,go straight thru the intersection of the environment 126 according tothe paths of a wait element. The trajectories of the two vehicles willintersect at one or more possible points along their trajectory, e.g.,the two trajectories may intersect over a relatively small set ofspatial points that are common to the respective trajectories, as thetrajectories are evolved, and the two vehicles continue progressing inseparate directions. In contrast, a merging intersection pattern mayoccur when two paths of a wait element there therefore the resultanttrajectories intersect, and continue along in similar directions, suchthat the trajectories intersect over a much larger set of spatialpoints. For example, a merging interference pattern may arise when thefirst vehicle 140 attempts to go straight through the intersection, andthe third vehicle 144 makes a right-hand turn through the intersectionat approximately a similar point in time that the first vehicle 140traverses the intersection. Merging interference patterns mayadditionally arise when one or more vehicles change lanes, navigates afreeway on-ramp such that a vehicle merges with pre-existing traffic onthe freeway, and the like.

When “forward simulating” the possible trajectories (e.g.,temporally-evolving) of a contender, the forward progress of thecontender's trajectory may be unconstrained if the contender has a legal(or normative) right of way. The first vehicle's 140 trajectory may beevolved forward as moving aggressively as reasonably appropriate. Forexample, an “aggressive trajectory” may be projected to determinewhether the first vehicle 140 may “clear” the intersection, well beforethe second vehicle 142 enters the intersection, such that anyintersection pattern in the trajectories occurs over disparate temporalperiods. If the “aggressive trajectory” does not result in clearing theintersection safely (and/or “politely”), then less aggressivetrajectories may be considered, e.g., a trajectory that models the firstvehicle 140 as slowing down, or even stopping at the intersection, maybe employed to select the yield behavior for the first vehicle 140.

In some embodiments, the yielding analysis is performed in discrete“chunks.” For example, each yield scenario is analyzed separately fromeach other yield scenario (e.g., successive intersections are analyzedseparately and/or independently from each other). For example,successive intersections may be analyzed serially in a first-infirst-out (FIFO) fashion. In the case of an intersection (such as butnot limited to the one illustrated in environment 126), at least twopossible yield behaviors may be analyzed. The first considered yieldbehavior may be a more aggressive yield behavior that includes the firstvehicle 140 navigating the intersection with a positive (but reasonable)acceleration. The second yield behavior may be a more conservative yieldbehavior that includes the first vehicle 140 employing a negativeacceleration (e.g., braking) to stop short of the intersection. Variousstrategies between these bimodal behaviors (e.g., the most aggressiveyield strategy or most conservative yield strategy) may be considered inthe various embodiments.

In one or more embodiments, each contender or contender path of a waitelement may be considered separately. In some embodiments, thecontenders or contender paths are analyzed, via parallel-computingmethods. In other embodiments, the contenders or contender paths areanalyzed serially, via the above mentioned FIFO analysis stack or queue.For example, the contender that is the earliest arrival to theintersection may be analyzed prior to the other contenders. The yieldinganalysis may be sufficiently forward in time, such that the firstvehicle 140 will not “block the intersection.” That is, in accordancewith the yield behavior, either the first vehicle 140 may safely clearthe intersection, or the first vehicle 140 may stop (or slows down)prior to entering the intersection, e.g., the first vehicle does notstop or brake in the middle of the intersection.

In addition to detecting the “beginning” of a yield scenario,embodiments may detect an “ending of the yield scenario.” For crossinginterference patterns, the end of a yield scenario may be signaled bythe first vehicle 140 successfully clearing the set of crossing pointsof the trajectories and/or contentions of one or more associated waitelements. For merging paths, the issue may be more intricate. Formerging interference patterns, a yield scenario may be terminated wheneach of the associated actors have reached an equilibrium state (e.g.,each of the actors have transitioned to a reasonable speed, consideringthe speed of the surrounding traffic, and each of the actors are spacedat a distance from the other actors that provides a reasonable level ofsafety.)

The functionality of the yield planner system 100 will now be discussedin a more detailed manner. Much of the following discussion is directedtowards structured yielding analysis in an intersection yieldingscenario that involves crossing interference patterns. However, theembodiments are not so limited, and it should be understood that suchmethods may be extended to include merging crossing patterns and/orholonomic contenders. It is also understood that the encoding of theyield scenario and detection of each of the actors' positions in arelevant phase space for the scenario may be performed by other systemsassociated with the first vehicle 140. Such information may be providedto the yield planner system 100 as inputs.

The yield behavior predictor 112 may analyze relationships in thelongitudinal (e.g., across a temporal dimension) evolution of eachactor's potential trajectories across a detected yield scenario, e.g.,the yield planner may perform time-forward trajectory simulations andtests for intersections of claimed sets. The yield planner system 100may model the environment 126 as a three-dimensional manifold (e.g., atop down view with two spatial dimensions with coordinates (x, y) and atemporal dimension with coordinate (t)). At each time t, the actors'claimed sets may be considered. Also at each t, the claimed sets may beprojected forward in time. Accordingly, the trajectories may be modeledto occur on a 4D locally-flat manifold (two spatial dimensions and twotemporal dimensions). The second temporal dimension may be referenced bythe coordinate z. Even though the two temporal dimensions may be relatedvia an affine transform, the two temporal dimensions may be keptseparate in the analysis to simplify projecting the trajectories. Thus,at each moment in time (t), each trajectory may be described by threecoordinates (x, y, z), where x and y refer to spatial coordinates and zrefers to a (forward-looking) time coordinate.

In performing the yield analysis, the yield behavior predictor 112 mayemploy the following simplifying assumptions. Firstly, the firstvehicle's 140 control system provides the yield planner system 100 thefirst vehicle's 140 planned and/or desired path embedded in the 4Dmanifold. Secondly, the control system is enabled to provide the yieldplanner system 100 close to complete information regarding eachcontender. That is, the yield planner system 100 can rely on receivingeach contender's current position in phase space (e.g., both locationand velocity), as well as a classification of each contender (e.g.,holonomic v non-holonomic) and at least an estimated shape and/or size(e.g., to generate a bounding box on the 2D spatial manifold (e.g., thespatial sub-manifold of the 4D space-time manifold) to model thephysical extensions of the contender). It may also be assumed that, foreach actor, the yield planner system 100 is provided a discrete finiteset of their possible intended paths. In practice, this may be handled,for example, by having a lane graph of the intersection or other yieldarea, including cross traffic paths, given by a map, and attached toperceived actors to those paths. In this way, possible options (e.g.,multiple trajectories) may be generated for each actor usingcorresponding wait elements that capture possible paths. Furthermore,each path of each contender may be considered separately be providing acorresponding wait element. This may simplify the analysis, such thatonly a set of two-body interactions need to be considered (e.g.,interference patterns between the first vehicle's 140 trajectory and atrajectory of a contender) when determining if a particularcontention/wait element is clear. That is, three or a greater number ofbody interactions need not be considered in the same analysis in someembodiments.

Any potential interference between the trajectories of the first vehicle140 and a contender (e.g., second vehicle 142 and/or third vehicle 144)may be based on an approximation of the rules or heuristics associatedwith navigating roadways. In particular, when considering a particularpairing of the first vehicle's 140 phase space coordinates (e.g., theposition and velocity coordinates along the first vehicle's currenttrajectory) and a contender's state (e.g., the position and velocitycoordinates along the contender's trajectory), it may be determinedwhether the trajectories cross or merge. Here, an actor's claimed setmay refer to the set of points in space that will be occupied by theactor's shape at some time if the actor starts braking now with someconstant safety braking deceleration. This scenario may be approximatedby analyzing progress along the trajectories, and also only consideringclaimed sets as progress along trajectories, while adding spatialpadding for width of actors to determine if an interference is likely tooccur.

The analysis may be subdivided into multiple stages. In a first stage,interference pairings between points on the paths in terms of crossings(e.g., represented as path interval pairs of a wait element that aredetermined to interfere as a whole) and merges (e.g., represented aspath interval pairs of a wait element that are determined to be thesame, corresponding 1:1 point per point) may be identified and/ordetermined. A second stage may consider how the actors and their claimedsets progress along their individual paths.

When determining whether an interference occurs, each of the actors maybe physically modeled as a polygon positioned on the 2D spatial manifoldand centered on the trajectory. That is, the spatial boundaries of eachactor may be modeled with a bounding box or shape that includes apolygonal footprint (although other shapes could be used). Thus, foreach considered trajectory, a set of polygons may be centered along thetrajectory, where the polygon extends beyond the trajectory toapproximate the spatial boundaries of the actor and extend along thetangential direction of the trajectory as the actor evolves in time. Theprogress along the first vehicle's 140 trajectory may be consideredseparately from the progress along the contender's trajectory. The setof potential interference points may be defined as a set of points thatare included in both of at least one of the first vehicle's 140 possiblepolygons and at least one of the contender's possible polygons, e.g.,the intersection of the polygons. From this set of potentialinterference points, a set of states along the first vehicle'strajectory and a set of states along the contender's trajectory may bedetermined. The set of states may be indicative of a physicalcontention. The time that each actor enters the interference region maybe determined from the set of states on both paths.

A first width and a first length may be assumed for the first vehicle's140 polygonal bounding box. Separate widths and lengths may be assumedfor the polygonal bounding box for each contender corresponding toreal-world dimensions of those actors. The yield behavior predictor 112may construct a first line of the first width, which may besubstantially perpendicular to the first vehicle's 140 trajectory thatis swept along the first vehicle's 140 trajectory. The yield behaviorpredictor 112 may also determine if the generated first line intersectsa similarly generated line for the contender's assumed width. Thecontender's line may be substantially perpendicular to the contender'strajectory, and swept along the contender's trajectory. The yieldbehavior predictor 112 may determine whether the two lines intersectwith each other. If an intersection occurs, a potential interference maybe assumed. In some embodiments, this analysis may be approximated byemploying a circle (rather than a polygon) with diameter equal to thefirst width along the first vehicle's 140 trajectory. The circle may becentered on the first vehicle's 140 trajectory and swept along thetrajectory as the first vehicle 140 is simulated forward in time. Pointson the contender trajectory that are included in at least one of thecircles may be identified.

Similarly, a sweeping circle may be generated for each contender, alongtheir respective trajectories, and all potential interference points maybe identified. This may be accomplished, for example, via nested closedloops. The yield behavior predictor 112 may subtract half the length ofthe first vehicle's 140 bounding box (e.g., whether the bounding box orshape is a polygon or a circle) to determine a point of entry and pointof exit for the interval of poses where the first vehicle's 140 boundingbox touches the interference zone. Merging interference patterns may besimilarly analyzed by finding extended intervals that overlap, or begiven directly from the lane graph.

When forward-projecting a trajectory, the distance traveled by a vehicle(e.g., the first vehicle 140) may be indicated as the function d(t) andthe velocity may be indicated as the function v(t). Thus, a temporalevolution of a trajectory through a vehicle's phase-space may beindicated via the 2D point (d(t), v(t)). It may be assumed that an actoraccelerates by some constant amount a (which can depend on actor classor whether the actor is going ahead aggressively or yielding, or be zeroif so desired) until reaching some maximum velocity (such as the speedlimit) or minimum velocity (such as stopping). A computation may beperformed and one or more arrays may be computed (e.g., for d(t) andv(t)) during a separate routine.

Given a vehicle's phase-space trajectories, the claimed sets may beconsidered at each time and whether they interfere at crossing ormerges. The claimed set corresponding to the state (d(t), v(t)) may bemodeled as decelerating by the fixed amount b (which may depend on actorclass) until stopped. The deceleration may take the form

${{z(t)} = \frac{v(t)}{b}},$

and follow the velocity profile v(t)−bz (note that the second temporalcoordinate is denoted by z to not confuse it with t, and there is anisomorphism between z and t). The stopping distance

$\frac{{v(t)}^{2}}{2b}$

may be added to d(t). Thus, the claimed set curve (as a function oft andz) may be calculated as:

D(t,z)=d(t)+v(t)·z−½·b·z² over the interval zϵ[0,v(t)b] and then stop atits maximum value of:

${d(t)} + {\frac{1}{2} \cdot {\frac{{v(t)}^{2}}{b}.}}$

Given the modeling of the various trajectories, the scenario analyzer110 may analyze the intersection (or crossing) as follows. For acrossing, each claimed set may be analyzed by determining the time thatthe claimed set enters the intersection 130 (e.g., z_(in)(t, D_(in)))and the time that the claimed set leaves the intersection (e.g.,z_(out)(t, D_(out))), where D_(in) may denote the distance along thepath at which the bounding box of the actor enters the crossing interval132 and D_(out) denotes the distance along the path at which thebounding box of the actor leaves the crossing interval 134 (e.g., in acase where the first vehicle 140 is going straight through theintersection 130. The entrance and exit times may be computed asfollows:

${{z_{in}\left( {t,D_{in}} \right)} = {\max\left( {0,{\frac{1}{b}\left\lbrack {{v(t)} - \sqrt{{v(t)}^{2} + {2{b\left( {{d(t)} - D_{in}} \right)}}}} \right\rbrack}} \right)}}{{z_{out}\left( {t,D_{out}} \right)} = {\max{\left( {0,{\frac{1}{b}\left\lbrack {{v(t)} - \sqrt{{v(t)}^{2} + {2{b\left( {{d(t)} - D_{out}} \right)}}}} \right\rbrack}} \right).}}}$

When the above calculated values are real, the values may be employed toidentify potential collisions in pairs of claimed sets, as discussedherein. If the value calculated for z_(in)(t, D_(in)) is complex, thenthe claimed set may not enter the crossing for this selection oft (andthus no potential collision). If the value calculated for z_(out)(t,D_(out)) is complex, then the claimed set may not leave the crossing forthis selection oft. The use of the max function clamps negative valuesto zero, since time-inversion symmetries are not contemplated in atleast some embodiments. The time interval [z_(in)(t, D_(in)),z_(out)(t,D_(out))] may be calculated for each claimed path, and foreach claimed path, an appropriate discretized set of t may beconsidered. The temporal interval [z_(in)(t, D_(in)), z_(out)(t,D_(out))], for the specific time bin t, may be referred to as az-interval for that time bin. A z-interval for the ego-vehicle may becalculated, as well as a z-interval for the contender path. Thez-interval may indicate the time period (according to the secondtemporal coordinate z, and for a particular value of the first temporalcoordinate t), that the actor is within the intersection. If theintersection of z-intervals for two claimed paths (e.g., theego-vehicle's claimed path and the contender's claimed path) is anon-null set, then the claimed paths may be associated with a potentialcollision. In such instances, a determined yield condition may include asignificant braking behavior to leave a good margin between the firstvehicle's 140 stopping point for the crossing and/or a wait element maybe determined as not cleared. If no overlap exists, the first vehicle140 may proceed accordingly and/or a wait element may be determined ascleared.

The scenario analyzer 110 may employ a separate method to determinewhether pairs of claimed sets indicated a potential collision formerging yielding scenarios. For merging yield scenarios, the scenarioanalyzer 110 may assume a distance to a merge point along the firstvehicle's 140 claimed path (e.g., D_(e_in)). Additionally, a distance tothe merge point for a contender's claimed path (e.g., D_(c_in)) may beassumed. After the merge point, the claimed paths may be assumed tocoincide. The scenario analyzer 110 may determine, whether if for any z,the interval is between the below function (e.g., a function of anon-negative value of z for a fixed value of t):

${{D_{e}\left( {t,z} \right)} - D_{e\_{in}}} = \left\{ {\begin{matrix}{{{d_{e}(t)} - D_{e\_{in}} + {{v_{e}(t)} \cdot z} - \frac{b_{e}z^{2}}{2}}\ ,\ {0 \leq z \leq \frac{v_{e}(t)}{b_{e}}}} \\{{d_{e} - D_{e\_{in}} + \frac{{v_{e}(t)}^{2}}{2 \cdot b_{e}}},\ {\frac{v_{e}(t)}{b_{e}} \leq z}}\end{matrix},} \right.$

which describes how far the front of the first vehicle's 140 claimed sethas passed the merge point (note that it is a quadratic followed by aconstant) and the vehicle length backwards (e.g., the interval may bedescribed by the function above and the same function minus the vehiclelength), is ever hit for a non-negative range value of the function:

${{D_{c}\left( {t,\ z} \right)} - D_{c\_{in}}} = \left\{ {\begin{matrix}{{{d_{c}(t)} - D_{c\_{in}} + {{v_{c}(t)} \cdot z} - \frac{b_{c}z^{2}}{2}}\ ,\ {0 \leq z \leq \frac{v_{c}(t)}{b_{c}}}} \\{{d_{c} - D_{c\_{in}} + \frac{{v_{c}(t)}^{2}}{2 \cdot b_{c}}},\ {\frac{v_{c}(t)}{b_{c}} \leq z}}\end{matrix},} \right.$

which describes how far the front of the claimed set of the contenderhas passed the merge point. Note that the subscripts e refer to thefirst vehicle 140 (e.g., the ego-vehicle) and the subscripts c refer toa contender in the merging yield scenario. This checks if the claimedsets intersect after the merge point. This can be determined by solvinga number of quadratics (solving for intersections between ego-front andcontender front and ego-back and contender front, which if done bruteforce requires solving 2×4=8 quadratics and reasoning about theintersection points) and taking care to handle all cases (ego vehicleclaimed set front and back reaches past the merge point, only frontreaches the merge point, or none reaches the merge point, and contenderfront reaches the merge point or not).

Now referring to FIGS. 3-7 , each block of methods 300-700, and othermethods described herein, comprises a computing process that may beperformed using any combination of hardware, firmware, and/or software.For instance, various functions may be carried out by a processorexecuting instructions stored in memory. The methods may also beembodied as computer-usable instructions stored on computer storagemedia. The methods may be provided by a standalone application, aservice or hosted service (standalone or in combination with anotherhosted service), or a plug-in to another product, to name a few. Inaddition, methods 300-700 are described, by way of example, with respectto the yield planer system of FIG. 1 . However, these methods mayadditionally or alternatively be executed by any one system, or anycombination of systems, including, but not limited to, those describedherein.

FIG. 3 is a flow diagram showing a method 300 for controlling anautonomous vehicle (e.g., an ego vehicle), in accordance with someembodiments of the present disclosure. The method 300, at block 302,includes detecting a yield scenario for the ego-vehicle. At block 304,one or more wait elements encoding various aspects of the yield scenarioare received. The one or more wait elements may be received in responseto detecting the yield scenario. The one or more wait elements mayencode one or more contenders relevant to the yield scenario. For eachcontender, the wait element may encode one or more claimed paths foreach indicated contender. The wait element may additionally encode atleast one claimed path for the ego-vehicle. At block 306, one or morecrossings (e.g., a crossing at an intersection) and/or mergings (e.g., alane merging), which are relevant to the yield scenario, are identifiedvia an analysis of the data encoded in the received wait elements.Various embodiments of identifying crossings and/or mergings arediscussed in conjunction with method 400 of FIG. 4 .

At block 308, trajectories are generated for the crossings and mergingsidentified in block 306 (e.g., from a wait element(s) paths). Variousembodiments of generating trajectories are discussed in conjunction withmethod 500 of FIG. 5 . At block 310, trajectories associated withcrossings are analyzed to determine whether a potential collisionbetween the ego-vehicle and one or more contenders are to be avoided ina crossings (or intersection) yield scenario. Various embodiments ofanalyzing crossing-related trajectories are discussed in conjunctionwith method 600 of FIG. 6 . Also at block 310, trajectories associatedwith mergings are analyzed to determine whether a potential collisionbetween the ego-vehicle and one or more contenders are to be avoided ina merging yield scenario. Various embodiments of analyzingcrossing-related trajectories are discussed in conjunction with methods600 and 700 of FIG. 6 and FIG. 7 respectively. However, briefly here,employing trajectories to analyze crossings and mergings may includeevaluating whether there is a conflict between the trajectory of theego-vehicle and one or more trajectories of the one or more contenders(e.g., evaluating whether there is a conflict between a first trajectory(of the ego-vehicle) and a second trajectory (of a contender)). At block312, a yield behavior is selected based on the analyses of the crossingand merging trajectories. At block 314, the ego-vehicle is controlled,such that the ego-vehicle operates with accordance to the yield behaviorselected at block 312.

FIG. 4 is a flow diagram showing a method 400 for identifying crossingsand merges for claimed paths for a yield scenario, in accordance withsome embodiments of the present disclosure. In some embodiments, themethod 400 may be called by the method 300 of FIG. 3 . For example, themethod 400 may be called from block 306 of the method 300. The method400, at block 402, includes receiving a set of claimed paths for theego-vehicle. Also at block 402, a set of claimed paths may be receivedfor each of the contenders relevant to a yield scenario (e.g., the yieldscenario detected in block 302 of the method 300). For example, thevarious sets of the claimed paths may be encoded in the one or more waitelements received in block 304 of the method 300. In at least oneembodiment, the set of claimed paths for the ego-vehicle may include asingle ego-vehicle claimed path and the set of the claimed paths foreach contender may include one or more claimed paths. At block 404, a“for” loop is initiated over the one or more contenders that arerelevant to the yield scenario. In various embodiments, rather than afor loop, a “while” loop, or any other such programmatic, repeating, oriterative, looping structures may be initiated in any of the methodsdiscussed herein.

At block 406, a for loop over the set of the claimed paths for thecurrent contender (of the contender for loop initiated at block 404) isinitiated. At block 408, a for loop is initiated over the discretizedclaimed points in the current contender path (of the contender pathloop). At block 410, a point on the ego-vehicle's claimed path that isclosest (e.g., according to an L2 norm for a Euclidean 2D manifold) tothe current point (of the path point for loop initiated at block 408) isidentified. At decision block 412, the distance (e.g., an L2 distance)between the contender path point and the ego-vehicle path point (e.g.,the point identified in block 410) is subject to a distance thresholdtest. In some embodiments, if the distance is greater than themax(ego-width, contender-width), where ego-width indicates the modeledwidth of the ego-vehicle and the modeled width of the contender, thenthere is no crossing or merging for the contender point and theego-vehicle path, and method 400 may proceed to decision block 416.

If the distance is less than the max (ego-width, contender-width), thenthere may be a crossing or merging for the contender point and theego-vehicle path, and the method 400 may proceed to decision block 414.Note that the modeled width of the ego-vehicle and the contenders may bedetermined using perception components of the ego-vehicle. At block 414,the ego-vehicle path and the contender path may be identified as aninterfering path pair. The path pair may be identified as a crossing ora merging interference path pair. In some embodiments, the relevant pairof points (e.g., the ego-vehicle path point and the contender pathpoint) may be identified as an interfering point pair (e.g., a crossingor merging interference point pair). The method 400 may flow to decisionblock 416.

At decision block 416, it is determined whether to terminate thecontender path points for loop. If the contender point's for loop is tobe terminated, then the method 400 flows to decision block 418.Otherwise, if the contender's point flow loop is to be continued, thenthe method 400 returns to block 408. At decision block 418, it isdetermined whether to terminate the contender's paths for loop. If thecontender's path for loop is to be terminated, then method 400 flows todecision block 420. If the contender's path for loop is to be continued,then method 400 returns to block 406. At decision block 420, it isdetermined whether to terminate the for loop over the contenders. If thefor loop over the contenders is to be terminated, the method 400 flowsto block 422. If the for loop over the contenders is to be terminated,then the method 400 returns to block 404. At block 422, the interferingpath pairs (and the interfering point pairs) may be returned. Forexample, the interfering path pairs may be returned as crossings andmerges to block 308 of the method 300.

FIG. 5 is a flow diagram showing a method 500 for generatingtrajectories for crossings and merges for a yield scenario, inaccordance with some embodiments of the present disclosure. In someembodiments, the method 500 may be called by the method 300 of FIG. 3 .For example, the method 500 may be called from block 308 of the method300. The method 500, at block 502, includes receiving the crossings andmergings. Receiving the crossings and mergings may include receiving theinterfering path pairs from the method 400 (e.g., block 422). At block504, a trajectory for the ego-vehicle may be generated. Theego-vehicle's trajectory may be through a velocity-distance phase-space,and may include determining (d_(e)(t), v_(e)(t)) as described herein.

At block 506, a for loop is initiated over the contenders. The for loopinitiated at block 506 may be limited to contenders that are involvedwith at least one interference (e.g., a crossing or a merging) with theego-vehicle path. At block 508, a for loop is initiated over the claimedpath of the current contender (e.g., with respect to the for loop ofblock 506). In some embodiments, the for loop may be limited to theclaimed paths that have an interference with the ego-vehicle path. Atblock 510, a trajectory for the current contender path (e.g., withrespect to the for loop of block 508) is generated. The contender path'strajectory may be through a velocity-distance phase-space, and mayinclude determining (d_(c)(t), v_(c)(t)) as described herein.

At decision block 512, it is determined whether to terminate thecontender's path loop. If the loop is to be terminated, the method 500flows to decision block 514. If the for loop is to be continued, themethods 500 may return to block 508. At decision block 514, it isdetermined whether to terminate the contenders for loop. If the for loopis to be terminated, the method 500 may flow to block 516. If the forloop is to be continued, then the method 500 may return to block 506. Atblock 516, the ego-vehicle trajectory and the contenders' trajectoriesare returned. For example, the trajectories may be returned as crossingand merge trajectories to block 310 of the method 300.

FIG. 6 is a flow diagram showing a method 600 for analyzing crossingtrajectories for a yield scenario, in accordance with some embodimentsof the present disclosure. In some embodiments, the method 600 may becalled by the method 300 of FIG. 3 . For example, the method 600 may becalled from block 310 of the method 300. The method 600, at block 602,includes receiving crossing-related trajectories. The crossing-relatedtrajectories may be returned from block 516 of the method 500. Thecrossing-related trajectories may include at least one ego-vehicletrajectory and one or more trajectories from one or more contenders.Each of the one or more contender trajectories may have one or morepoints that are associated with one or more crossing interferences withone or more points of the ego-vehicle trajectory. At block 604, a forloop over the one or more contenders is initiated. At block 608, a forloop over the current contender's trajectories is initiated. The forloop of block 608 may be limited to the current contender's trajectoriesthat have at least one crossing interference with the ego-vehicletrajectory. At block 610, a for loop over the discretized time bins ofthe current contender trajectory is initiated.

At block 612, the z-interval for the ego-vehicle (for the current t binvalue) is calculated as described herein, e.g., [z_(in)(t, D_(in)),z_(out) (t, D_(out))]. Note that the interval calculated at block 612are the z-intervals calculated by the scenario analyzer 110 of FIG. 1 .At block 614, the z-interval for the current contender path (and for thecurrent t bin value) is calculated. At decision block 616, it isdetermined whether there is an overlap between the z-interval for theego-vehicle path and the contender path. If the overlap is the null set,then the method 600 may flow to decision block 620. If the overlap ofthe two z-intervals is a non-null set, then the method 600 may flow toblock 618. At block 618, a potential crossing collision may be logged orstored for the pair of trajectories at the particular t bin value. Themethod 600 may then proceed to decision block 620.

At decision block 620, it is determined whether to terminate the forloop over the t bin values. If the loop is to be terminated, then themethod 600 flows to block 622. If the loop is not to be terminated, thenthe method 600 returns to block 610. At decision block 622, it isdetermined whether to terminate the loop over the trajectory's crossinginterferences. If the loop is to be terminated, the method 600 mayproceed to decision block 624. If the loop is not terminated, then themethod 600 returns to block 608. At decision block 624, it is determinedwhether to terminate the loop under the current contender'strajectories. If the loop is to be terminated, the method 600 flows todecision block 626. If the loop is not to be terminated, then the method600 returns to block 606. At decision block 626, it is determinedwhether to terminate the loop over the contenders. If the loop is to beterminated, then the method 600 may flow to block 628. If the loop isnot to be terminated, then the method 600 may return to block 604. Atblock 628, the potential crossing collisions are returned (e.g., thoseinterferences where the contender trajectory and the ego-vehicletrajectory have overlapping z-intervals for at least one particularvalue of the time bin). The potential crossing collisions may bereturned to block 312 of the method 300.

FIG. 7 is a flow diagram showing a method 700 for analyzing mergingtrajectories for a yield scenario, in accordance with some embodimentsof the present disclosure. In some embodiments, the method 700 may becalled by the method 300 of FIG. 3 . For example, the method 700 may becalled from block 310 of the method 300. The method 700, at block 702,includes receiving merging-related trajectories. The merging-relatedtrajectories, or merging trajectories, may be returned from block 516 ofthe method 500. The merging trajectories may include at least oneego-vehicle trajectory and one or more trajectories from one or morecontenders. Each of the one or more contender trajectories may have oneor more points that are associated with one or more merginginterferences with one or more points of the ego-vehicle trajectory. Atblock 704, a for loop over the one or more contenders is initiated. Atblock 708, a for loop over the current contender's trajectories isinitiated. The for loop of block 708 may be limited to the currentcontender's trajectories that have at least one merging interferencewith the ego-vehicle trajectory. At block 710, a for loop over thediscretized time bins of the current contender trajectory is initiated.

At decision block 712, it is determined if the z claimed sets intersect.Note, the z claimed sets are those calculated by the scenario analyzer110 of FIG. 1 . Thus, the z-intervals include the determination of(D_(e) (t,z)−D_(e_in)) (for the ego-vehicle trajectory) and(D_(c)(t,z)−D_(c_in)) (for the contender trajectory), as describedabove. If there is no intersection, then the method 700 may flow todecision block 716. If there is an intersection, then the method 700 mayflow to block 712. At block 714, a potential merging collision may belogged or stored for the pair of trajectories at the particular t binvalue. The method 700 may then proceed to decision block 716.

At decision block 716, it is determined whether to terminate the forloop over the t bin values. If the loop is to be terminated, then themethod 700 flows to decision block 718. If the loop is not to beterminated, then the method 700 returns to block 710. At decision block718, it is determined whether to terminate the loop over thetrajectory's merging interferences. If the loop is to be terminated, themethod 700 may proceed to decision block 720. If the loop is notterminated, then the method 700 returns to block 708. At decision block720, it is determined whether to terminate the loop under the currentcontender's trajectories. If the loop is to be terminated, the method700 flows to decision block 722. If the loop is not to be terminated,then the method 700 returns to block 706. At decision block 722, it isdetermined whether to terminate the loop over the contenders. If theloop is to be terminated, then the method 700 may flow to block 724. Ifthe loop is not to be terminated, then the method 700 may return toblock 704. At block 724, the potential merging collisions are returned(e.g., those interferences where the contender trajectory and theego-vehicle trajectory have intersecting z claimed sets for at least oneparticular value of the time bin). The potential crossing collisions maybe returned to block 312 of the method 300.

Example Autonomous Vehicle

FIG. 8A is an illustration of an example autonomous vehicle 800, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 800 (alternatively referred to herein as the “vehicle800”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,a vehicle coupled to a trailer, and/or another type of vehicle (e.g.,that is unmanned and/or that accommodates one or more passengers).Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 800 may be capable offunctionality in accordance with one or more of Level 3-Level 5 of theautonomous driving levels. For example, the vehicle 800 may be capableof conditional automation (Level 3), high automation (Level 4), and/orfull automation (Level 5), depending on the embodiment.

The vehicle 800 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 800 may include a propulsion system850, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 850 may be connected to a drive train of the vehicle800, which may include a transmission, to enable the propulsion of thevehicle 800. The propulsion system 850 may be controlled in response toreceiving signals from the throttle/accelerator 852.

A steering system 854, which may include a steering wheel, may be usedto steer the vehicle 800 (e.g., along a desired path or route) when thepropulsion system 850 is operating (e.g., when the vehicle is inmotion). The steering system 854 may receive signals from a steeringactuator 856. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 846 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 848 and/or brakesensors.

Controller(s) 836, which may include one or more system on chips (SoCs)804 (FIG. 8C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle800. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 848, to operate thesteering system 854 via one or more steering actuators 856, to operatethe propulsion system 850 via one or more throttle/accelerators 852. Thecontroller(s) 836 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 800. The controller(s) 836 may include a first controller 836for autonomous driving functions, a second controller 836 for functionalsafety functions, a third controller 836 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 836 forinfotainment functionality, a fifth controller 836 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 836 may handle two or more of the abovefunctionalities, two or more controllers 836 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 836 may provide the signals for controlling one ormore components and/or systems of the vehicle 800 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 858 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDARsensor(s) 864, inertial measurement unit (IMU) sensor(s) 866 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 896, stereo camera(s) 868, wide-view camera(s) 870(e.g., fisheye cameras), infrared camera(s) 872, surround camera(s) 874(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 898,speed sensor(s) 844 (e.g., for measuring the speed of the vehicle 800),vibration sensor(s) 842, steering sensor(s) 840, brake sensor(s) (e.g.,as part of the brake sensor system 846), and/or other sensor types.

One or more of the controller(s) 836 may receive inputs (e.g.,represented by input data) from an instrument cluster 832 of the vehicle800 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 834, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle800. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 822 of FIG. 8C), location data(e.g., the vehicle's 800 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 836,etc. For example, the HMI display 834 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 800 further includes a network interface 824 which may useone or more wireless antenna(s) 826 and/or modem(s) to communicate overone or more networks. For example, the network interface 824 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 826 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 8B is an example of camera locations and fields of view for theexample autonomous vehicle 800 of FIG. 8A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle800.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 800. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 800 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 836 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (LDW), Autonomous Cruise Control(ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 870 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.8B, there may any number of wide-view cameras 870 on the vehicle 800. Inaddition, long-range camera(s) 898 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 898 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 868 may also be included in a front-facingconfiguration. The stereo camera(s) 868 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 868 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 868 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 800 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 874 (e.g., four surround cameras 874 asillustrated in FIG. 8B) may be positioned to on the vehicle 800. Thesurround camera(s) 874 may include wide-view camera(s) 870, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 874 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 800 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 898,stereo camera(s) 868), infrared camera(s) 872, etc.), as describedherein.

FIG. 8C is a block diagram of an example system architecture for theexample autonomous vehicle 800 of FIG. 8A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 800 in FIG.8C are illustrated as being connected via bus 802. The bus 802 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 800 used to aid in control of various features and functionalityof the vehicle 800, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 802 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 802, this is notintended to be limiting. For example, there may be any number of busses802, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses802 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 802 may be used for collisionavoidance functionality and a second bus 802 may be used for actuationcontrol. In any example, each bus 802 may communicate with any of thecomponents of the vehicle 800, and two or more busses 802 maycommunicate with the same components. In some examples, each SoC 804,each controller 836, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle800), and may be connected to a common bus, such the CAN bus.

The vehicle 800 may include one or more controller(s) 836, such as thosedescribed herein with respect to FIG. 8A. The controller(s) 836 may beused for a variety of functions. The controller(s) 836 may be coupled toany of the various other components and systems of the vehicle 800, andmay be used for control of the vehicle 800, artificial intelligence ofthe vehicle 800, infotainment for the vehicle 800, and/or the like.

The vehicle 800 may include a system(s) on a chip (SoC) 804. The SoC 804may include CPU(s) 806, GPU(s) 808, processor(s) 810, cache(s) 812,accelerator(s) 814, data store(s) 816, and/or other components andfeatures not illustrated. The SoC(s) 804 may be used to control thevehicle 800 in a variety of platforms and systems. For example, theSoC(s) 804 may be combined in a system (e.g., the system of the vehicle800) with an HD map 822 which may obtain map refreshes and/or updatesvia a network interface 824 from one or more servers (e.g., server(s)878 of FIG. 8D).

The CPU(s) 806 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 806 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 806may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 806 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 806 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)806 to be active at any given time.

The CPU(s) 806 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 806may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 808 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 808 may be programmable and may beefficient for parallel workloads. The GPU(s) 808, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 808 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 808 may include at least eight streamingmicroprocessors. The GPU(s) 808 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 808 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 808 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 808 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 808 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 808 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 808 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 808 to access the CPU(s) 806 page tables directly. Insuch examples, when the GPU(s) 808 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 806. In response, the CPU(s) 806 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 808. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 806 and the GPU(s) 808, thereby simplifying the GPU(s) 808programming and porting of applications to the GPU(s) 808.

In addition, the GPU(s) 808 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 808 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 804 may include any number of cache(s) 812, including thosedescribed herein. For example, the cache(s) 812 may include an L3 cachethat is available to both the CPU(s) 806 and the GPU(s) 808 (e.g., thatis connected both the CPU(s) 806 and the GPU(s) 808). The cache(s) 812may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 804 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 800—such as processingDNNs. In addition, the SoC(s) 804 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 806 and/or GPU(s) 808.

The SoC(s) 804 may include one or more accelerators 814 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 804 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 808 and to off-load some of the tasks of theGPU(s) 808 (e.g., to free up more cycles of the GPU(s) 808 forperforming other tasks). As an example, the accelerator(s) 814 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 808, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 808 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 808 and/or other accelerator(s) 814.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 806. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 814. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 804 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 814 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 866 output thatcorrelates with the vehicle 800 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 864 or RADAR sensor(s) 860), amongothers.

The SoC(s) 804 may include data store(s) 816 (e.g., memory). The datastore(s) 816 may be on-chip memory of the SoC(s) 804, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 816 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 812 may comprise L2 or L3 cache(s) 812. Reference to thedata store(s) 816 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 814, as described herein.

The SoC(s) 804 may include one or more processor(s) 810 (e.g., embeddedprocessors). The processor(s) 810 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 804 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 804 thermals and temperature sensors, and/ormanagement of the SoC(s) 804 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 804 may use thering-oscillators to detect temperatures of the CPU(s) 806, GPU(s) 808,and/or accelerator(s) 814. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 804 into a lower powerstate and/or put the vehicle 800 into a chauffeur to safe stop mode(e.g., bring the vehicle 800 to a safe stop).

The processor(s) 810 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 810 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 810 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 810 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 810 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 810 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)870, surround camera(s) 874, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 808 is not required tocontinuously render new surfaces. Even when the GPU(s) 808 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 808 to improve performance and responsiveness.

The SoC(s) 804 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 804 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 804 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 804 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 864, RADAR sensor(s) 860,etc. that may be connected over Ethernet), data from bus 802 (e.g.,speed of vehicle 800, steering wheel position, etc.), data from GNSSsensor(s) 858 (e.g., connected over Ethernet or CAN bus). The SoC(s) 804may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 806 from routine data management tasks.

The SoC(s) 804 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 804 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 814, when combined with the CPU(s) 806, the GPU(s) 808,and the data store(s) 816, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 820) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 808.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 800. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 804 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 896 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 804 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)858. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 862, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 818 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 804 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 818 may include an X86 processor,for example. The CPU(s) 818 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 804, and/or monitoring the statusand health of the controller(s) 836 and/or infotainment SoC 830, forexample.

The vehicle 800 may include a GPU(s) 820 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 804 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 820 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 800.

The vehicle 800 may further include the network interface 824 which mayinclude one or more wireless antennas 826 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 824 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 878 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 800information about vehicles in proximity to the vehicle 800 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 800).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 800.

The network interface 824 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 836 tocommunicate over wireless networks. The network interface 824 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 800 may further include data store(s) 828 which may includeoff-chip (e.g., off the SoC(s) 804) storage. The data store(s) 828 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 800 may further include GNSS sensor(s) 858. The GNSSsensor(s) 858 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS)sensors, etc.), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)858 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 800 may further include RADAR sensor(s) 860. The RADARsensor(s) 860 may be used by the vehicle 800 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 860 may usethe CAN and/or the bus 802 (e.g., to transmit data generated by theRADAR sensor(s) 860) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 860 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 860 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 860may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 800 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 800 lane.

Mid-range RADAR systems may include, as an example, a range of up to 860m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 850 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 800 may further include ultrasonic sensor(s) 862. Theultrasonic sensor(s) 862, which may be positioned at the front, back,and/or the sides of the vehicle 800, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 862 may be used, and different ultrasonic sensor(s) 862 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 862 may operate at functional safety levels of ASILB.

The vehicle 800 may include LIDAR sensor(s) 864. The LIDAR sensor(s) 864may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 864 maybe functional safety level ASIL B. In some examples, the vehicle 800 mayinclude multiple LIDAR sensors 864 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 864 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 864 may have an advertised rangeof approximately 800m, with an accuracy of 2 cm-3 cm, and with supportfor a 800 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 864 may be used. In such examples,the LIDAR sensor(s) 864 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 800.The LIDAR sensor(s) 864, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)864 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 800. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)864 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 866. The IMU sensor(s) 866may be located at a center of the rear axle of the vehicle 800, in someexamples. The IMU sensor(s) 866 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 866 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 866 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 866 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 866 may enable the vehicle 800to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 866. In some examples, the IMU sensor(s) 866 and theGNSS sensor(s) 858 may be combined in a single integrated unit.

The vehicle may include microphone(s) 896 placed in and/or around thevehicle 800. The microphone(s) 896 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 868, wide-view camera(s) 870, infrared camera(s) 872,surround camera(s) 874, long-range and/or mid-range camera(s) 898,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 800. The types of cameras useddepends on the embodiments and requirements for the vehicle 800, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 800. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 8A and FIG. 8B.

The vehicle 800 may further include vibration sensor(s) 842. Thevibration sensor(s) 842 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 842 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 800 may include an ADAS system 838. The ADAS system 838 mayinclude a SoC, in some examples. The ADAS system 838 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 860, LIDAR sensor(s) 864, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 800 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 800 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 824 and/or the wireless antenna(s) 826 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 800), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 800, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle800 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 800 if the vehicle 800 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 800 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 860, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 800, the vehicle 800itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 836 or a second controller 836). For example, in someembodiments, the ADAS system 838 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 838may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 804.

In other examples, ADAS system 838 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 838 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 838indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 800 may further include the infotainment SoC 830 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 830 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 800. For example, the infotainment SoC 830 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 834, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 830 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 838,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 830 may include GPU functionality. The infotainmentSoC 830 may communicate over the bus 802 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 800. Insome examples, the infotainment SoC 830 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 836(e.g., the primary and/or backup computers of the vehicle 800) fail. Insuch an example, the infotainment SoC 830 may put the vehicle 800 into achauffeur to safe stop mode, as described herein.

The vehicle 800 may further include an instrument cluster 832 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 832 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 832 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 830 and theinstrument cluster 832. In other words, the instrument cluster 832 maybe included as part of the infotainment SoC 830, or vice versa.

FIG. 8D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 800 of FIG. 8A, inaccordance with some embodiments of the present disclosure. The system876 may include server(s) 878, network(s) 890, and vehicles, includingthe vehicle 800. The server(s) 878 may include a plurality of GPUs884(A)-884(H) (collectively referred to herein as GPUs 884), PCIeswitches 882(A)-882(H) (collectively referred to herein as PCIe switches882), and/or CPUs 880(A)-880(B) (collectively referred to herein as CPUs880). The GPUs 884, the CPUs 880, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 888 developed by NVIDIA and/orPCIe connections 886. In some examples, the GPUs 884 are connected viaNVLink and/or NVSwitch SoC and the GPUs 884 and the PCIe switches 882are connected via PCIe interconnects. Although eight GPUs 884, two CPUs880, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 878 mayinclude any number of GPUs 884, CPUs 880, and/or PCIe switches. Forexample, the server(s) 878 may each include eight, sixteen, thirty-two,and/or more GPUs 884.

The server(s) 878 may receive, over the network(s) 890 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 878 may transmit, over the network(s) 890 and to the vehicles,neural networks 892, updated neural networks 892, and/or map information894, including information regarding traffic and road conditions. Theupdates to the map information 894 may include updates for the HD map822, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 892, the updated neural networks 892, and/or the mapinformation 894 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 878 and/or other servers).

The server(s) 878 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 890, and/or the machine learningmodels may be used by the server(s) 878 to remotely monitor thevehicles.

In some examples, the server(s) 878 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 878 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 884, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 878 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 878 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 800. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 800, suchas a sequence of images and/or objects that the vehicle 800 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 800 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 800 is malfunctioning, the server(s) 878 may transmit asignal to the vehicle 800 instructing a fail-safe computer of thevehicle 800 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 878 may include the GPU(s) 884 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 9 is a block diagram of an example computing device(s) 900 suitablefor use in implementing some embodiments of the present disclosure.Computing device 900 may include an interconnect system 902 thatdirectly or indirectly couples the following devices: memory 904, one ormore central processing units (CPUs) 906, one or more graphicsprocessing units (GPUs) 908, a communication interface 910, input/output(I/O) ports 912, input/output components 914, a power supply 916, one ormore presentation components 918 (e.g., display(s)), and one or morelogic units 920. In at least one embodiment, the computing device(s) 900may comprise one or more virtual machines (VMs), and/or any of thecomponents thereof may comprise virtual components (e.g., virtualhardware components). For non-limiting examples, one or more of the GPUs908 may comprise one or more vGPUs, one or more of the CPUs 906 maycomprise one or more vCPUs, and/or one or more of the logic units 920may comprise one or more virtual logic units. As such, a computingdevice(s) 900 may include discrete components (e.g., a full GPUdedicated to the computing device 900), virtual components (e.g., aportion of a GPU dedicated to the computing device 900), or acombination thereof.

Although the various blocks of FIG. 9 are shown as connected via theinterconnect system 902 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 918, such as a display device, may be consideredan I/O component 914 (e.g., if the display is a touch screen). Asanother example, the CPUs 906 and/or GPUs 908 may include memory (e.g.,the memory 904 may be representative of a storage device in addition tothe memory of the GPUs 908, the CPUs 906, and/or other components). Inother words, the computing device of FIG. 9 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.9 .

The interconnect system 902 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 902 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 906 may be directly connectedto the memory 904. Further, the CPU 906 may be directly connected to theGPU 908. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 902 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 900.

The memory 904 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 900. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 904 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device900. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 906 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 900 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 906 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 906 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 900 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 900, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 900 mayinclude one or more CPUs 906 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 906, the GPU(s) 908 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device900 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 908 may be an integrated GPU (e.g.,with one or more of the CPU(s) 906 and/or one or more of the GPU(s) 908may be a discrete GPU. In embodiments, one or more of the GPU(s) 908 maybe a coprocessor of one or more of the CPU(s) 906. The GPU(s) 908 may beused by the computing device 900 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 908 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 908may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 908may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 906 received via ahost interface). The GPU(s) 908 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory904. The GPU(s) 908 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 908 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 906 and/or the GPU(s)908, the logic unit(s) 920 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 900 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 906, the GPU(s)908, and/or the logic unit(s) 920 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 920 may be part of and/or integrated in one ormore of the CPU(s) 906 and/or the GPU(s) 908 and/or one or more of thelogic units 920 may be discrete components or otherwise external to theCPU(s) 906 and/or the GPU(s) 908. In embodiments, one or more of thelogic units 920 may be a coprocessor of one or more of the CPU(s) 906and/or one or more of the GPU(s) 908.

Examples of the logic unit(s) 920 include one or more processing coresand/or components thereof, such as Data Processing Units (DPUs), TensorCores (TCs), Tensor Processing Units(TPUs), Pixel Visual Cores (PVCs),Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs),Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs),Tree Traversal Units (TTUs), Artificial Intelligence Accelerators(AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units(ALUs), Application-Specific Integrated Circuits (ASICs), Floating PointUnits (FPUs), input/output (I/O) elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The communication interface 910 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 900to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 910 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet. In one or more embodiments, logic unit(s) 920and/or communication interface 910 may include one or more dataprocessing units (DPUs) to transmit data received over a network and/orthrough interconnect system 902 directly to (e.g., a memory of) one ormore GPU(s) 908.

The I/O ports 912 may enable the computing device 900 to be logicallycoupled to other devices including the I/O components 914, thepresentation component(s) 918, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 900.Illustrative I/O components 914 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 914 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 900. Thecomputing device 900 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 900 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 900 to render immersive augmented reality or virtual reality.

The power supply 916 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 916 may providepower to the computing device 900 to enable the components of thecomputing device 900 to operate.

The presentation component(s) 918 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 918 may receivedata from other components (e.g., the GPU(s) 908, the CPU(s) 906, DPUs,etc.), and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG. 10 illustrates an example data center 1000 that may be used in atleast one embodiments of the present disclosure. The data center 1000may include a data center infrastructure layer 1010, a framework layer1020, a software layer 1030, and/or an application layer 1040.

As shown in FIG. 10 , the data center infrastructure layer 1010 mayinclude a resource orchestrator 1012, grouped computing resources 1014,and node computing resources (“node C.R.s”) 1016(1)-1016(N), where “N”represents any whole, positive integer. In at least one embodiment, nodeC.R.s 1016(1)-1016(N) may include, but are not limited to, any number ofcentral processing units (CPUs) or other processors (including DPUs,accelerators, field programmable gate arrays (FPGAs), graphicsprocessors or graphics processing units (GPUs), etc.), memory devices(e.g., dynamic read-only memory), storage devices (e.g., solid state ordisk drives), network input/output (NW I/O) devices, network switches,virtual machines (VMs), power modules, and/or cooling modules, etc. Insome embodiments, one or more node C.R.s from among node C.R.s1016(1)-1016(N) may correspond to a server having one or more of theabove-mentioned computing resources. In addition, in some embodiments,the node C.R.s 1016(1)-10161(N) may include one or more virtualcomponents, such as vGPUs, vCPUs, and/or the like, and/or one or more ofthe node C.R.s 1016(1)-1016(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 1014 may includeseparate groupings of node C.R.s 1016 housed within one or more racks(not shown), or many racks housed in data centers at variousgeographical locations (also not shown). Separate groupings of nodeC.R.s 1016 within grouped computing resources 1014 may include groupedcompute, network, memory or storage resources that may be configured orallocated to support one or more workloads. In at least one embodiment,several node C.R.s 1016 including CPUs, GPUs, DPUs, and/or otherprocessors may be grouped within one or more racks to provide computeresources to support one or more workloads. The one or more racks mayalso include any number of power modules, cooling modules, and/ornetwork switches, in any combination.

The resource orchestrator 1012 may configure or otherwise control one ormore node C.R.s 1016(1)-1016(N) and/or grouped computing resources 1014.In at least one embodiment, resource orchestrator 1012 may include asoftware design infrastructure (SDI) management entity for the datacenter 1000. The resource orchestrator 1012 may include hardware,software, or some combination thereof.

In at least one embodiment, as shown in FIG. 10 , framework layer 1020may include a job scheduler 1033, a configuration manager 1034, aresource manager 1036, and/or a distributed file system 1038. Theframework layer 1020 may include a framework to support software 1032 ofsoftware layer 1030 and/or one or more application(s) 1042 ofapplication layer 1040. The software 1032 or application(s) 1042 mayrespectively include web-based service software or applications, such asthose provided by Amazon Web Services, Google Cloud and Microsoft Azure.The framework layer 1020 may be, but is not limited to, a type of freeand open-source software web application framework such as Apache Spark™(hereinafter “Spark”) that may utilize distributed file system 1038 forlarge-scale data processing (e.g., “big data”). In at least oneembodiment, job scheduler 1033 may include a Spark driver to facilitatescheduling of workloads supported by various layers of data center 1000.The configuration manager 1034 may be capable of configuring differentlayers such as software layer 1030 and framework layer 1020 includingSpark and distributed file system 1038 for supporting large-scale dataprocessing. The resource manager 1036 may be capable of managingclustered or grouped computing resources mapped to or allocated forsupport of distributed file system 1038 and job scheduler 1033. In atleast one embodiment, clustered or grouped computing resources mayinclude grouped computing resource 1014 at data center infrastructurelayer 1010. The resource manager 1036 may coordinate with resourceorchestrator 1012 to manage these mapped or allocated computingresources.

In at least one embodiment, software 1032 included in software layer1030 may include software used by at least portions of node C.R.s1016(1)-1016(N), grouped computing resources 1014, and/or distributedfile system 1038 of framework layer 1020. One or more types of softwaremay include, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 1042 included in applicationlayer 1040 may include one or more types of applications used by atleast portions of node C.R.s 1016(1)-1016(N), grouped computingresources 1014, and/or distributed file system 1038 of framework layer1020. One or more types of applications may include, but are not limitedto, any number of a genomics application, a cognitive compute, and amachine learning application, including training or inferencingsoftware, machine learning framework software (e.g., PyTorch,TensorFlow, Caffe, etc.), and/or other machine learning applicationsused in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager 1034, resourcemanager 1036, and resource orchestrator 1012 may implement any numberand type of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. Self-modifying actions mayrelieve a data center operator of data center 1000 from making possiblybad configuration decisions and possibly avoiding underutilized and/orpoor performing portions of a data center.

The data center 1000 may include tools, services, software or otherresources to train one or more machine learning models or predict orinfer information using one or more machine learning models according toone or more embodiments described herein. For example, a machinelearning model(s) may be trained by calculating weight parametersaccording to a neural network architecture using software and/orcomputing resources described above with respect to the data center1000. In at least one embodiment, trained or deployed machine learningmodels corresponding to one or more neural networks may be used to inferor predict information using resources described above with respect tothe data center 1000 by using weight parameters calculated through oneor more training techniques, such as but not limited to those describedherein.

In at least one embodiment, the data center 1000 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, and/orother hardware (or virtual compute resources corresponding thereto) toperform training and/or inferencing using above-described resources.Moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of thecomputing device(s) 900 of FIG. 9 —e.g., each device may include similarcomponents, features, and/or functionality of the computing device(s)900. In addition, where backend devices (e.g., servers, NAS, etc.) areimplemented, the backend devices may be included as part of a datacenter 1000, an example of which is described in more detail herein withrespect to FIG. 10 .

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example computing device(s) 900described herein with respect to FIG. 9 . By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: identifying a scenario for afirst vehicle based at least on analyzing sensor data generated by atleast one sensor of the first vehicle in an environment; receiving await element associated with the scenario, the wait element encoding afirst path for the first vehicle to traverse an area in the environment,and a second path for a second vehicle to traverse the area;determining, from the first path, a first trajectory in the area for thefirst vehicle based at least on a first location of the first vehicle ata time; determining, from the second path, a second trajectory in thearea for the second vehicle based at least on a second location of thesecond vehicle at the time; evaluating whether there is a conflictbetween the first trajectory and the second trajectory; and transmittingdata that causes the first vehicle to operate in accordance with a waitstate based at least on the evaluating, wherein the wait state defines ayielding behavior for the first vehicle.
 2. The method of claim 1,wherein the time is a first time and the method is further comprisingidentifying the conflict between the first trajectory and the secondtrajectory based on determining an intersection between at least aportion of a first claimed set of the first vehicle starting from asecond time on the first trajectory and at least a portion of a secondclaimed set of the second vehicle starting from the second time on thesecond trajectory.
 3. The method of claim 2, wherein determining theintersection is based at least on identifying a potential intersectionbetween a first bounding box representing a geometry of the firstvehicle traversing the first trajectory and a second bounding boxrepresenting a geometry of the second vehicle traversing the secondtrajectory.
 4. The method of claim 1, further comprising: determining afirst time interval that indicates a first set of temporal coordinatescharacterizing the first trajectory traversing the area; determining asecond time interval that indicates a second set of temporal coordinatescharacterizing the second trajectory traversing the area; determining anintersection of the first set of temporal coordinates and the second setof temporal coordinates; and identifying the conflict between the firsttrajectory and the second trajectory based at least on detecting theintersection of the first set of temporal coordinates and the second setof temporal coordinates is a non-null set.
 5. The method of claim 1,wherein the first trajectory and the second trajectory are eachtrajectories within a four-dimensional manifold with at least twospatial dimensions and at least two temporal dimensions.
 6. The methodof claim 1, wherein the scenario includes one or more of a crossingscenario with the area including an intersection or a merging scenariowith the area including merging lanes.
 7. The method of claim 1, whereinthe second trajectory simulates the second vehicle accelerating afterthe time.
 8. A processor comprising: one or more circuits to: identify ascenario for a first vehicle based at least on analyzing sensor datagenerated by at least one sensor of the first vehicle in an environment;receive a wait element associated with the scenario, the wait elementencoding a first path for the first vehicle to traverse an area in theenvironment, and a second path for a second vehicle to traverse thearea; employ the first path to determine a first trajectory in the areafor the first vehicle based at least on a first location of the firstvehicle at a time; employ the second path to determine a secondtrajectory in the area for the second vehicle based at least on a secondlocation of the second vehicle at the time; evaluate whether there is aconflict between the first trajectory and the second trajectory; andtransmitting data that causes the first vehicle to operate in accordancewith a wait state based at least on the evaluating, wherein the waitstate defines a yielding behavior for the first vehicle.
 9. Theprocessor of claim 8, wherein the time is a first time and the one ormore circuits are further to identify the conflict between the firsttrajectory and the second trajectory based on determining anintersection between at least a portion of a first claimed set of thefirst vehicle starting from a second time on the first trajectory and atleast a portion of a second claimed set of the second vehicle startingfrom the second time on the second trajectory.
 10. The processor ofclaim 9, wherein determining the intersection is based on identifying apotential intersection between a first bounding box representing ageometry of the first vehicle and a second bounding box representing ageometry of the second vehicle.
 11. The processor claim 8, wherein theone or more circuits are further to: determine a first time intervalthat indicates a first set of temporal coordinates characterizing thefirst trajectory traversing the area; determine a second time intervalthat indicates a second set of temporal coordinates characterizing thesecond trajectory traversing the area; determine an intersection of thefirst set of temporal coordinates and the second set of temporalcoordinates; and determine the conflict between the first trajectory andthe second trajectory based on detecting that the intersection of thefirst set of temporal coordinates and the second set of temporalcoordinates is a non-null set.
 12. The processor of claim 8, wherein thefirst trajectory and the second trajectory are each trajectories withina four-dimensional manifold with at least two spatial dimensions and atleast two temporal dimensions.
 13. The processor of claim 8, wherein thescenario includes one or more of a crossing scenario with the areaincluding an intersection or a merging scenario with the area includingmerging lanes.
 14. The processor of claim 8, wherein the secondtrajectory simulates the second vehicle accelerating after the time. 15.A system comprising: one or more processing units; and one or morememory devices storing instructions that, when executed using the one ormore processing units, cause the one or more processing units to executeactions comprising: identifying a scenario for a first vehicle based atleast on analyzing sensor data generated by at least one sensor of thefirst vehicle in an environment; receiving a wait element associatedwith the scenario, the wait element encoding a first path for the firstvehicle to traverse a area in the environment, and a second path for asecond vehicle to traverse the area; determining, from the first path, afirst trajectory in the area for the first vehicle based at least on afirst location of the first vehicle at a time; determining, from thesecond path, a second trajectory in the area for the second vehiclebased at least on a second location of the second vehicle at the time;evaluating whether there is a conflict between the first trajectory andthe second trajectory; and transmitting data that causes the firstvehicle to operate in accordance with a wait state based at least on theevaluating, wherein the wait state defines a yielding behavior for thefirst vehicle.
 16. The system of claim 15, wherein the time is a firsttime and the actions further comprise identifying the conflict betweenthe first trajectory and the second trajectory based on determining anintersection between at least a portion of a first claimed set of thefirst vehicle starting from a second time on the first trajectory and atleast a portion of a second claimed set of the second vehicle startingfrom the second time on the second trajectory.
 17. The system of claim16, wherein determining the intersection is based on identifying apotential intersection between a first bounding box representing ageometry of the first vehicle and a second bounding box representing ageometry of the second vehicle.
 18. The system of claim 15, the actionsfurther comprising: determining a first time interval that indicates afirst set of temporal coordinates characterizing the first trajectorytraversing the area; determining a second time interval that indicates asecond set of temporal coordinates characterizing the second trajectorytraversing the area; determining an intersection of the first set oftemporal coordinates and the second set of temporal coordinates; andidentifying the conflict between the first trajectory and the secondtrajectory based on detecting that the intersection of the first set oftemporal coordinates and the second set of temporal coordinates is anon-null set.
 19. The system of claim 15, wherein the first trajectoryand the second trajectory are each trajectories within afour-dimensional manifold with at least two spatial dimensions and atleast two temporal dimensions.
 20. The system of claim 15, comprised inat least one of: a control system for an autonomous or semi-autonomousmachine; a perception system for an autonomous or semi-autonomousmachine; a system for performing simulation operations; a system forperforming deep learning operations; a system implemented using an edgedevice; a system implemented using a robot; a system incorporating oneor more virtual machines (VMs); a system implemented at least partiallyin a data center; or a system implemented at least partially using cloudcomputing resources.